Image deblurring.  
Early works on image deblurring assume a uniform blur kernel and mainly focus on designing effective image priors [1]}, [2]}, [3]}, [4]} to regularize the deblurring process into a well-posed problem.
Recent deep learning models rely on paired sharp and blurry images for supervised training.
In particular, the methods [5]}, [6]} learn to model the non-uniform blur kernels.
Tao  [7]} design a recurrent structure to iteratively refine the deblurring results.
Zhang  [8]} propose a spatially invariant recurrent network to remove blurs in the feature space.
Gao  [9]} propose a nested skip connection structure with selective parameter sharing.
These models show promising deblurring results on the synthetic blurry images.
The work [10]} attempts to model the realistic blurs from real-life blurred images.
Most learning-based works are trained with the typical reconstruction loss,  \(L_1\)  or \(L_2\)  losses, while [11]} uses an additional adversarial loss.
